Nonlinear Support Vector Machines

Nonlinear Support Vector Machines is a machine learning model that extends the support vector machine framework by employing kernel functions to enable the separation of nonlinearly separable data through implicit mapping into a higher-dimensional feature space.

689

Publications

47.8K

Citations

2.1K

Authors

813

Institutions

Publications per year

2017–2026

200

Authors

2.1K

  1. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    8 publications · 1.3K citations

    8

    h-index

  2. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    6 publications · 490 citations

    6

    h-index

  3. OL
    O. L. Mangasarian

    University of Wisconsin–Madison, United States

    6 publications · 1.7K citations

    6

    h-index

  4. XL
    Xinjiang Lu

    Central South University, China

    6 publications · 252 citations

    6

    h-index

  5. LJ
    Licheng Jiao

    Xidian University, China

    5 publications · 655 citations

    5

    h-index

1–5 of 2.1K

Venues

  1. Neurocomputing

    18 publications · 993 citations

    14

    h-index

  2. IEEE Transactions on Neural Networks

    13 publications · 1.2K citations

    12

    h-index

  3. Expert Systems with Applications

    12 publications · 859 citations

    12

    h-index

  4. Pattern Recognition

    11 publications · 571 citations

    11

    h-index

  5. Neural Networks

    7 publications · 2.8K citations

    7

    h-index

1–5